import json
import copy
import time
import random
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import matplotlib.pyplot as plt
from matplotlib import pyplot as plt
from torchsummary import summary
from nmfd_gnn import NMFD_GNN
print (torch.cuda.is_available())
device = torch.device("cuda:0")
random_seed = 42
random.seed(random_seed)
torch.manual_seed(random_seed)
torch.cuda.manual_seed(random_seed)
r = random.random
True
#1.1: settings
M = 20 #number of time interval in a window
missing_ratio = 0.50
file_name = "m_" + str(M) + "_missing_" + str(int(missing_ratio*100))
print (file_name)
#1.2: hyperparameters
num_epochs, batch_size, learning_rate = 200, 16, 0.001
beta_flow, beta_occ, beta_phy = 1.0, 1.0, 0.1
batch_size_vt = 16 #batch size for evaluation and test
hyper = {"n_e": num_epochs, "b_s": batch_size, "b_s_vt": batch_size_vt, "l_r": learning_rate,\
"beta_f": beta_flow, "beta_o": beta_occ, "beta_p": beta_phy}
gnn_dim_1, gnn_dim_2, gnn_dim_3, lstm_dim = 2, 128, 128, 128
p_dim = 10 #column dimension of L1, L2
c_k = 5.5 #meter, the sum of loop width and uniform vehicle length. based on Gero and Daganzo 2008.
theta_ini = [-2.879, 5.207, -2.473, 1.722, 3.619]
hyper_model = {"g_dim_1": gnn_dim_1, "g_dim_2": gnn_dim_2, "g_dim_3": gnn_dim_3, "l_dim": lstm_dim,\
"p_dim": p_dim, "c_k": c_k, "theta_ini": theta_ini}
max_no_decrease = 30
#1.3: set paths
root_path = "/home/umni2/a/umnilab/users/xue120/umni4/2023_mfd_traffic_london/"
file_path = root_path + "2_prepare_data/" + file_name + "/"
train_path, vali_path, test_path =\
file_path + "train.json", file_path + "vali.json", file_path + "test.json"
sensor_id_path = file_path + "sensor_id_order.json"
sensor_adj_path = file_path + "sensor_adj.json"
mean_std_path = file_path + "mean_std.json"
m_20_missing_50
def visualize_train_loss(total_phy_flow_occ_loss):
plt.figure(figsize=(4,3), dpi=75)
t_p_f_o_l = np.array(total_phy_flow_occ_loss)
e_loss, p_loss, f_loss, o_loss = t_p_f_o_l[:,0], t_p_f_o_l[:,1], t_p_f_o_l[:,2], t_p_f_o_l[:,3]
x = range(len(e_loss))
plt.plot(x, p_loss, linewidth=1, label = "phy loss")
plt.plot(x, f_loss, linewidth=1, label = "flow loss")
plt.plot(x, o_loss, linewidth=1, label = "occ loss")
plt.legend()
plt.title('Loss decline on train')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.savefig(file_name + '/' + 'train_loss.png', bbox_inches = 'tight')
plt.show()
def visualize_flow_loss(vali_f_mae, test_f_mae):
plt.figure(figsize=(4,3), dpi=75)
x = range(len(vali_f_mae))
plt.plot(x, vali_f_mae, linewidth=1, label="Validate")
plt.plot(x, test_f_mae, linewidth=1, label="Test")
plt.legend()
plt.title('MAE of flow on validate/test')
plt.xlabel('Epoch')
plt.ylabel('MAE (veh/h)')
plt.savefig(file_name + '/' + 'flow_mae.png', bbox_inches = 'tight')
plt.show()
def visualize_occ_loss(vali_o_mae, test_o_mae):
plt.figure(figsize=(4,3), dpi=75)
x = range(len(vali_o_mae))
plt.plot(x, vali_o_mae, linewidth=1, label="Validate")
plt.plot(x, test_o_mae, linewidth=1, label="Test")
plt.legend()
plt.title('MAE of occupancy on validate/test')
plt.xlabel('Epoch')
plt.ylabel('MAE')
plt.savefig(file_name + '/' + 'occ_mae.png',bbox_inches = 'tight')
plt.show()
def MAELoss(yhat, y):
return float(torch.mean(torch.div(torch.abs(yhat-y), 1)))
def RMSELoss(yhat, y):
return float(torch.sqrt(torch.mean((yhat-y)**2)))
def vali_test(model, f, f_mask, o, o_mask, f_o_mean_std, b_s_vt):
flow_std, occ_std, n = f_o_mean_std[1], f_o_mean_std[3], len(f)
f_mae_list, f_rmse_list, o_mae_list, o_rmse_list, num_list = list(), list(), list(), list(), list()
for i in range(0, n, b_s_vt):
s, e = i, np.min([i+b_s_vt, n])
num_list.append(e-s)
bf, bo, bf_mask, bo_mask = f[s: e], o[s: e], f_mask[s: e], o_mask[s: e]
bf_hat, bo_hat, bq_hat, bq_theta = model.run(bf_mask, bo_mask)
bf_hat, bo_hat = bf_hat.cpu(), bo_hat.cpu()
bf_mae, bf_rmse = MAELoss(bf_hat, bf)*flow_std, RMSELoss(bf_hat, bf)*flow_std
bo_mae, bo_rmse = MAELoss(bo_hat, bo)*occ_std, RMSELoss(bo_hat, bo)*occ_std
f_mae_list.append(bf_mae)
f_rmse_list.append(bf_rmse)
o_mae_list.append(bo_mae)
o_rmse_list.append(bo_rmse)
f_mae, o_mae = np.dot(f_mae_list, num_list)/n, np.dot(o_mae_list, num_list)/n
f_rmse = np.sqrt(np.dot(np.multiply(f_rmse_list, f_rmse_list), num_list)/n)
o_rmse = np.sqrt(np.dot(np.multiply(o_rmse_list, o_rmse_list), num_list)/n)
return f_mae, f_rmse, o_mae, o_rmse
def evaluate(model, vt_f, vt_o, vt_f_m, vt_o_m, f_o_mean_std, b_s_vt): #vt: vali_test
vt_f_mae, vt_f_rmse, vt_o_mae, vt_o_rmse =\
vali_test(model, vt_f, vt_f_m, vt_o, vt_o_m, f_o_mean_std, b_s_vt)
return vt_f_mae, vt_f_rmse, vt_o_mae, vt_o_rmse
#4.1: one training epoch
def train_epoch(model, opt, criterion, train_f_x, train_f_y, train_o_x, train_o_y, hyper, flow_std_squ):
#f: flow; o: occupancy
model.train()
losses, p_losses, f_losses, o_losses = list(), list(), list(), list()
beta_f, beta_o, beta_p, b_s = hyper["beta_f"], hyper["beta_o"], hyper["beta_p"], hyper["b_s"]
n = len(train_f_x)
print ("# batch: ", int(n/b_s))
for i in range(0, n-b_s, b_s):
time1 = time.time()
x_f_batch, y_f_batch = train_f_x[i: i+b_s], train_f_y[i: i+b_s]
x_o_batch, y_o_batch = train_o_x[i: i+b_s], train_o_y[i: i+b_s]
opt.zero_grad()
y_f_hat, y_o_hat, q_hat, q_theta = model.run(x_f_batch, x_o_batch)
p_loss = criterion(q_hat, q_theta).cpu() #physical loss
p_loss = p_loss/flow_std_squ
f_loss = criterion(y_f_hat.cpu(), y_f_batch) #data loss of flow
o_loss = criterion(y_o_hat.cpu(), y_o_batch) #data loss of occupancy
loss = beta_f*f_loss + beta_o*o_loss + beta_p*p_loss
loss.backward()
opt.step()
losses.append(loss.data.numpy())
p_losses.append(p_loss.data.numpy())
f_losses.append(f_loss.data.numpy())
o_losses.append(o_loss.data.numpy())
if i % (64*b_s) == 0:
print ("i_batch: ", i/b_s)
print ("the loss for this batch: ", loss.data.numpy())
print ("flow loss", f_loss.data.numpy())
print ("occ loss", o_loss.data.numpy())
time2 = time.time()
print ("time for this batch", time2-time1)
print ("----------------------------------")
n_loss = float(len(losses)+0.000001)
aver_loss = sum(losses)/n_loss
aver_p_loss = sum(p_losses)/n_loss
aver_f_loss = sum(f_losses)/n_loss
aver_o_loss = sum(o_losses)/n_loss
return aver_loss, model, aver_p_loss, aver_f_loss, aver_o_loss
#4.2: all train epochs
def train_process(model, criterion, train, vali, test, hyper, f_o_mean_std):
total_phy_flow_occ_loss = list()
n_mse_flow_occ = 0 #mse(flow) + mse(occ) for validation sets.
vali_f, vali_o = vali["flow"], vali["occupancy"]
vali_f_m, vali_o_m = vali["flow_mask"].to(device), vali["occupancy_mask"].to(device)
test_f, test_o = test["flow"], test["occupancy"]
test_f_m, test_o_m = test["flow_mask"].to(device), test["occupancy_mask"].to(device)
l_r, n_e = hyper["l_r"], hyper["n_e"]
opt = optim.Adam(model.parameters(), l_r, betas = (0.9,0.999), weight_decay=0.0001)
opt_scheduler = torch.optim.lr_scheduler.MultiStepLR(opt, milestones=[150])
print ("# epochs ", n_e)
r_vali_f_mae, r_vali_o_mae, r_test_f_mae, r_test_o_mae = list(), list(), list(), list()
r_vali_f_rmse, r_vali_o_rmse, r_test_f_rmse, r_test_o_rmse = list(), list(), list(), list()
flow_std_squ = np.power(f_o_mean_std[1], 2)
no_decrease = 0
for i in range(n_e):
print ("----------------an epoch starts-------------------")
#time1_s = time.time()
time_s = time.time()
print ("i_epoch: ", i)
n_train = len(train["flow"])
number_list = copy.copy(list(range(n_train)))
random.shuffle(number_list, random = r)
shuffle_idx = torch.tensor(number_list)
train_x_f, train_y_f = train["flow_mask"][shuffle_idx], train["flow"][shuffle_idx]
train_x_o, train_y_o = train["occupancy_mask"][shuffle_idx], train["occupancy"][shuffle_idx]
aver_loss, model, aver_p_loss, aver_f_loss, aver_o_loss =\
train_epoch(model, opt, criterion, train_x_f.to(device), train_y_f,\
train_x_o.to(device), train_y_o, hyper, flow_std_squ)
opt_scheduler.step()
total_phy_flow_occ_loss.append([aver_loss, aver_p_loss, aver_f_loss, aver_o_loss])
print ("train loss for this epoch: ", round(aver_loss, 6))
#evaluate
b_s_vt = hyper["b_s_vt"]
vali_f_mae, vali_f_rmse, vali_o_mae, vali_o_rmse =\
evaluate(model, vali_f, vali_o, vali_f_m, vali_o_m, f_o_mean_std, b_s_vt)
test_f_mae, test_f_rmse, test_o_mae, test_o_rmse =\
evaluate(model, test_f, test_o, test_f_m, test_o_m, f_o_mean_std, b_s_vt)
r_vali_f_mae.append(vali_f_mae)
r_test_f_mae.append(test_f_mae)
r_vali_o_mae.append(vali_o_mae)
r_test_o_mae.append(test_o_mae)
r_vali_f_rmse.append(vali_f_rmse)
r_test_f_rmse.append(test_f_rmse)
r_vali_o_rmse.append(vali_o_rmse)
r_test_o_rmse.append(test_o_rmse)
visualize_train_loss(total_phy_flow_occ_loss)
visualize_flow_loss(r_vali_f_mae, r_test_f_mae)
visualize_occ_loss(r_vali_o_mae, r_test_o_mae)
time_e = time.time()
print ("time for this epoch", time_e - time_s)
performance = {"train": total_phy_flow_occ_loss,\
"vali": [r_vali_f_mae, r_vali_f_rmse, r_vali_o_mae, r_vali_o_rmse],\
"test": [r_test_f_mae, r_test_f_rmse, r_test_o_mae, r_test_o_rmse]}
subfile = open(file_name + '/' + 'performance'+'.json','w')
json.dump(performance, subfile)
subfile.close()
#early stop
flow_std, occ_std = f_o_mean_std[1], f_o_mean_std[3]
norm_f_rmse, norm_o_rmse = vali_f_rmse/flow_std, vali_o_rmse/occ_std
norm_sum_mse = norm_f_rmse*norm_f_rmse + norm_o_rmse*norm_o_rmse
if n_mse_flow_occ > 0:
min_until_now = min([min_until_now, norm_sum_mse])
else:
min_until_now = 1000000.0
if norm_sum_mse > min_until_now:
no_decrease = no_decrease+1
else:
no_decrease = 0
if no_decrease == max_no_decrease:
print ("Early stop at the " + str(i+1) + "-th epoch")
return total_phy_flow_occ_loss, model
n_mse_flow_occ = n_mse_flow_occ + 1
print ("No_decrease: ", no_decrease)
return total_phy_flow_occ_loss, model
def tensorize(train_vali_test):
result = dict()
result["flow"] = torch.tensor(train_vali_test["flow"])
result["flow_mask"] = torch.tensor(train_vali_test["flow_mask"])
result["occupancy"] = torch.tensor(train_vali_test["occupancy"])
result["occupancy_mask"] = torch.tensor(train_vali_test["occupancy_mask"])
return result
def normalize_flow_occ(tvt, f_o_mean_std): #tvt: train, vali, test
#flow
f_mean, f_std = f_o_mean_std[0], f_o_mean_std[1]
f_mask, f = tvt["flow_mask"], tvt["flow"]
tvt["flow_mask"] = ((np.array(f_mask)-f_mean)/f_std).tolist()
tvt["flow"] = ((np.array(f)-f_mean)/f_std).tolist()
#occ
o_mean, o_std = f_o_mean_std[2], f_o_mean_std[3]
o_mask, o = tvt["occupancy_mask"], tvt["occupancy"]
tvt["occupancy_mask"] = ((np.array(o_mask)-o_mean)/o_std).tolist()
tvt["occupancy"] = ((np.array(o)-o_mean)/o_std).tolist()
return tvt
def transform_distance(d_matrix):
sigma, n_row, n_col = np.std(d_matrix), len(d_matrix), len(d_matrix[0])
sigma_square = sigma*sigma
for i in range(n_row):
for j in range(n_col):
d_i_j = d_matrix[i][j]
d_matrix[i][j] = np.exp(0.0-10000.0*d_i_j*d_i_j/sigma_square)
return d_matrix
def load_data(train_path, vali_path, test_path, sensor_adj_path, mean_std_path, sensor_id_path):
mean_std = json.load(open(mean_std_path))
f_mean, f_std, o_mean, o_std =\
mean_std["f_mean"], mean_std["f_std"], mean_std["o_mean"], mean_std["o_std"]
f_o_mean_std = [f_mean, f_std, o_mean, o_std]
train = json.load(open(train_path))
vali = json.load(open(vali_path))
test = json.load(open(test_path))
adj = json.load(open(sensor_adj_path))["adj"]
n_sensor = len(train["flow"][0])
train = tensorize(normalize_flow_occ(train, f_o_mean_std))
vali = tensorize(normalize_flow_occ(vali, f_o_mean_std))
test = tensorize(normalize_flow_occ(test, f_o_mean_std))
adj = torch.tensor(transform_distance(adj), device=device).float()
df_sensor_id = json.load(open(sensor_id_path))
sensor_length = [0.0 for i in range(n_sensor)]
for sensor in df_sensor_id:
sensor_length[df_sensor_id[sensor][0]] = df_sensor_id[sensor][3]
return train, vali, test, adj, n_sensor, f_o_mean_std, sensor_length
#6.1 load the data
time1 = time.time()
train, vali, test, adj, n_sensor, f_o_mean_std, sensor_length =\
load_data(train_path, vali_path, test_path, sensor_adj_path, mean_std_path, sensor_id_path)
time2 = time.time()
print (time2-time1)
13.775065660476685
print (len(train["flow"]))
print (len(vali["flow"]))
print (len(test["flow"]))
print (f_o_mean_std)
1536 499 500 [425.68492811748513, 254.84583261239152, 0.1814023556701015, 0.18315625109655478]
model = NMFD_GNN(n_sensor, M, hyper_model, f_o_mean_std, sensor_length, adj).to(device)
cri = nn.MSELoss()
#6.2: train the model
total_phy_flow_occ_loss, trained_model = train_process(model, cri, train, vali, test, hyper, f_o_mean_std)
# epochs 200 ----------------an epoch starts------------------- i_epoch: 0 # batch: 96 i_batch: 0.0 the loss for this batch: 1.9815644 flow loss 1.0990472 occ loss 0.6143677 time for this batch 0.8666303157806396 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.7851688 flow loss 0.28925544 occ loss 0.26341987 time for this batch 0.46655821800231934 ---------------------------------- train loss for this epoch: 0.90803
time for this epoch 56.600130796432495 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 1 # batch: 96 i_batch: 0.0 the loss for this batch: 0.62810546 flow loss 0.22280373 occ loss 0.19019218 time for this batch 0.5049211978912354 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.5521626 flow loss 0.17682189 occ loss 0.16271259 time for this batch 0.482083797454834 ---------------------------------- train loss for this epoch: 0.576578
time for this epoch 57.2423734664917 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 2 # batch: 96 i_batch: 0.0 the loss for this batch: 0.51837295 flow loss 0.17848775 occ loss 0.14021462 time for this batch 0.44369935989379883 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.5815691 flow loss 0.17008369 occ loss 0.20409842 time for this batch 0.4835498332977295 ---------------------------------- train loss for this epoch: 0.519876
time for this epoch 56.98795461654663 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 3 # batch: 96 i_batch: 0.0 the loss for this batch: 0.46400177 flow loss 0.1502272 occ loss 0.1344724 time for this batch 0.3854334354400635 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.4384641 flow loss 0.14810973 occ loss 0.11199102 time for this batch 0.4764838218688965 ---------------------------------- train loss for this epoch: 0.494977
time for this epoch 56.59439134597778 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 4 # batch: 96 i_batch: 0.0 the loss for this batch: 0.45155627 flow loss 0.14722879 occ loss 0.12919635 time for this batch 0.4362785816192627 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.45014727 flow loss 0.13063583 occ loss 0.12899676 time for this batch 0.4861299991607666 ---------------------------------- train loss for this epoch: 0.476882
time for this epoch 56.48940825462341 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 5 # batch: 96 i_batch: 0.0 the loss for this batch: 0.4881399 flow loss 0.12328085 occ loss 0.13712935 time for this batch 0.4240257740020752 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.41624188 flow loss 0.13219951 occ loss 0.11508801 time for this batch 0.37302088737487793 ---------------------------------- train loss for this epoch: 0.467109
time for this epoch 56.74505114555359 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 6 # batch: 96 i_batch: 0.0 the loss for this batch: 0.41717216 flow loss 0.12227805 occ loss 0.117699414 time for this batch 0.473649263381958 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.43921518 flow loss 0.10907964 occ loss 0.1155334 time for this batch 0.4767031669616699 ---------------------------------- train loss for this epoch: 0.459195
time for this epoch 56.73500633239746 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 7 # batch: 96 i_batch: 0.0 the loss for this batch: 0.45677063 flow loss 0.120371975 occ loss 0.1309238 time for this batch 0.45988893508911133 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.4023338 flow loss 0.10447434 occ loss 0.112420246 time for this batch 0.4799065589904785 ---------------------------------- train loss for this epoch: 0.451609
time for this epoch 56.56524610519409 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 8 # batch: 96 i_batch: 0.0 the loss for this batch: 0.40440255 flow loss 0.11971536 occ loss 0.10398293 time for this batch 0.47215843200683594 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.41753185 flow loss 0.1121397 occ loss 0.11227685 time for this batch 0.49344325065612793 ---------------------------------- train loss for this epoch: 0.445805
time for this epoch 57.16133451461792 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 9 # batch: 96 i_batch: 0.0 the loss for this batch: 0.5015305 flow loss 0.1374961 occ loss 0.14968869 time for this batch 0.43357348442077637 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.45673352 flow loss 0.12625226 occ loss 0.12890202 time for this batch 0.5211546421051025 ---------------------------------- train loss for this epoch: 0.442471
time for this epoch 56.98993992805481 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 10 # batch: 96 i_batch: 0.0 the loss for this batch: 0.42497647 flow loss 0.11038421 occ loss 0.12952758 time for this batch 0.4687023162841797 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.37421432 flow loss 0.100800306 occ loss 0.10845154 time for this batch 0.49625158309936523 ---------------------------------- train loss for this epoch: 0.437229
time for this epoch 58.26401424407959 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 11 # batch: 96 i_batch: 0.0 the loss for this batch: 0.4526253 flow loss 0.11580003 occ loss 0.124270596 time for this batch 0.5035843849182129 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.41458207 flow loss 0.09952807 occ loss 0.11918813 time for this batch 0.44874095916748047 ---------------------------------- train loss for this epoch: 0.437693
time for this epoch 57.39416003227234 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 12 # batch: 96 i_batch: 0.0 the loss for this batch: 0.41939002 flow loss 0.11180738 occ loss 0.109392464 time for this batch 0.44814205169677734 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.48002583 flow loss 0.11422182 occ loss 0.13683493 time for this batch 0.4520280361175537 ---------------------------------- train loss for this epoch: 0.432869
time for this epoch 57.5204963684082 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 13 # batch: 96 i_batch: 0.0 the loss for this batch: 0.38461804 flow loss 0.10329538 occ loss 0.108999565 time for this batch 0.45218849182128906 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.45933563 flow loss 0.11341537 occ loss 0.12214761 time for this batch 0.4942970275878906 ---------------------------------- train loss for this epoch: 0.432196
time for this epoch 56.69869017601013 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 14 # batch: 96 i_batch: 0.0 the loss for this batch: 0.40595698 flow loss 0.09306713 occ loss 0.121114634 time for this batch 0.4209275245666504 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.3917174 flow loss 0.10045648 occ loss 0.098613 time for this batch 0.3889462947845459 ---------------------------------- train loss for this epoch: 0.429507
time for this epoch 56.719913959503174 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 15 # batch: 96 i_batch: 0.0 the loss for this batch: 0.50059026 flow loss 0.13346553 occ loss 0.14473838 time for this batch 0.43703699111938477 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.41173792 flow loss 0.09790888 occ loss 0.1107974 time for this batch 0.5008053779602051 ---------------------------------- train loss for this epoch: 0.428269
time for this epoch 58.0811653137207 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 16 # batch: 96 i_batch: 0.0 the loss for this batch: 0.4487177 flow loss 0.1217096 occ loss 0.13021946 time for this batch 0.47139453887939453 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.37587398 flow loss 0.09616328 occ loss 0.099565595 time for this batch 0.475430965423584 ---------------------------------- train loss for this epoch: 0.425522
time for this epoch 57.16891145706177 No_decrease: 3 ----------------an epoch starts------------------- i_epoch: 17 # batch: 96 i_batch: 0.0 the loss for this batch: 0.39768016 flow loss 0.09809334 occ loss 0.115026265 time for this batch 0.38155078887939453 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.36976495 flow loss 0.10229407 occ loss 0.09544969 time for this batch 0.45798540115356445 ---------------------------------- train loss for this epoch: 0.424612
time for this epoch 56.704729318618774 No_decrease: 4 ----------------an epoch starts------------------- i_epoch: 18 # batch: 96 i_batch: 0.0 the loss for this batch: 0.40041277 flow loss 0.100135855 occ loss 0.11654389 time for this batch 0.44441986083984375 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.3532248 flow loss 0.09398451 occ loss 0.09037387 time for this batch 0.4970591068267822 ---------------------------------- train loss for this epoch: 0.424012
time for this epoch 57.04270911216736 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 19 # batch: 96 i_batch: 0.0 the loss for this batch: 0.40563178 flow loss 0.102993906 occ loss 0.10871507 time for this batch 0.4410436153411865 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.3790532 flow loss 0.10664109 occ loss 0.10246803 time for this batch 0.4953880310058594 ---------------------------------- train loss for this epoch: 0.418903
time for this epoch 56.91793918609619 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 20 # batch: 96 i_batch: 0.0 the loss for this batch: 0.46053943 flow loss 0.11645563 occ loss 0.13180608 time for this batch 0.4135167598724365 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.46676612 flow loss 0.111099504 occ loss 0.14263982 time for this batch 0.49423861503601074 ---------------------------------- train loss for this epoch: 0.419397
time for this epoch 57.122206926345825 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 21 # batch: 96 i_batch: 0.0 the loss for this batch: 0.44766033 flow loss 0.10425908 occ loss 0.12036569 time for this batch 0.47788000106811523 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.36561 flow loss 0.09193448 occ loss 0.094230026 time for this batch 0.4685022830963135 ---------------------------------- train loss for this epoch: 0.417096
time for this epoch 57.15723776817322 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 22 # batch: 96 i_batch: 0.0 the loss for this batch: 0.3960529 flow loss 0.11642271 occ loss 0.097140916 time for this batch 0.4330790042877197 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.32736588 flow loss 0.082311854 occ loss 0.07472456 time for this batch 0.503929615020752 ---------------------------------- train loss for this epoch: 0.417185
time for this epoch 58.30890250205994 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 23 # batch: 96 i_batch: 0.0 the loss for this batch: 0.29416412 flow loss 0.07743814 occ loss 0.07481889 time for this batch 0.456514835357666 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.44421843 flow loss 0.10066111 occ loss 0.13487573 time for this batch 0.5155096054077148 ---------------------------------- train loss for this epoch: 0.416196
time for this epoch 58.81370425224304 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 24 # batch: 96 i_batch: 0.0 the loss for this batch: 0.3819264 flow loss 0.09463833 occ loss 0.10117159 time for this batch 0.4426436424255371 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.37747034 flow loss 0.10364902 occ loss 0.09680381 time for this batch 0.47710180282592773 ---------------------------------- train loss for this epoch: 0.41354
time for this epoch 57.96373701095581 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 25 # batch: 96 i_batch: 0.0 the loss for this batch: 0.41961646 flow loss 0.10611949 occ loss 0.1056734 time for this batch 0.46695661544799805 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.35486275 flow loss 0.088980764 occ loss 0.10239354 time for this batch 0.5151512622833252 ---------------------------------- train loss for this epoch: 0.41402
time for this epoch 60.22601008415222 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 26 # batch: 96 i_batch: 0.0 the loss for this batch: 0.46689218 flow loss 0.102170326 occ loss 0.13366473 time for this batch 0.4547107219696045 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.43256992 flow loss 0.10371715 occ loss 0.1287532 time for this batch 0.45818090438842773 ---------------------------------- train loss for this epoch: 0.412368
time for this epoch 59.83414816856384 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 27 # batch: 96 i_batch: 0.0 the loss for this batch: 0.42484623 flow loss 0.11783598 occ loss 0.10917192 time for this batch 0.4482707977294922 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.37923363 flow loss 0.095129766 occ loss 0.10827326 time for this batch 0.4892551898956299 ---------------------------------- train loss for this epoch: 0.411682
time for this epoch 58.671732664108276 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 28 # batch: 96 i_batch: 0.0 the loss for this batch: 0.409368 flow loss 0.09869444 occ loss 0.11390136 time for this batch 0.46148180961608887 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.42496687 flow loss 0.10907376 occ loss 0.10124661 time for this batch 0.519437313079834 ---------------------------------- train loss for this epoch: 0.41005
time for this epoch 57.84042167663574 No_decrease: 3 ----------------an epoch starts------------------- i_epoch: 29 # batch: 96 i_batch: 0.0 the loss for this batch: 0.42307884 flow loss 0.10574784 occ loss 0.10804686 time for this batch 0.37985754013061523 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.371911 flow loss 0.09575754 occ loss 0.08779247 time for this batch 0.494673490524292 ---------------------------------- train loss for this epoch: 0.41097
time for this epoch 56.76196360588074 No_decrease: 4 ----------------an epoch starts------------------- i_epoch: 30 # batch: 96 i_batch: 0.0 the loss for this batch: 0.34918404 flow loss 0.08914035 occ loss 0.09551398 time for this batch 0.4486820697784424 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.4072982 flow loss 0.090297215 occ loss 0.105377026 time for this batch 0.5140800476074219 ---------------------------------- train loss for this epoch: 0.406818
time for this epoch 57.503493785858154 No_decrease: 5 ----------------an epoch starts------------------- i_epoch: 31 # batch: 96 i_batch: 0.0 the loss for this batch: 0.42254722 flow loss 0.11159819 occ loss 0.116882145 time for this batch 0.45699381828308105 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.38102853 flow loss 0.09489956 occ loss 0.09742557 time for this batch 0.5091052055358887 ---------------------------------- train loss for this epoch: 0.40425
time for this epoch 58.36080598831177 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 32 # batch: 96 i_batch: 0.0 the loss for this batch: 0.44825068 flow loss 0.09335173 occ loss 0.124251954 time for this batch 0.47090792655944824 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.4397977 flow loss 0.11403872 occ loss 0.116409406 time for this batch 0.4982187747955322 ---------------------------------- train loss for this epoch: 0.402007
time for this epoch 57.02274537086487 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 33 # batch: 96 i_batch: 0.0 the loss for this batch: 0.3771222 flow loss 0.09125594 occ loss 0.10766024 time for this batch 0.44614505767822266 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.4594447 flow loss 0.0996757 occ loss 0.1369935 time for this batch 0.4827277660369873 ---------------------------------- train loss for this epoch: 0.398187
time for this epoch 56.94504451751709 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 34 # batch: 96 i_batch: 0.0 the loss for this batch: 0.4342507 flow loss 0.12288104 occ loss 0.1304513 time for this batch 0.43971776962280273 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.4104241 flow loss 0.091517225 occ loss 0.10836525 time for this batch 0.5395078659057617 ---------------------------------- train loss for this epoch: 0.393426
time for this epoch 57.39520287513733 No_decrease: 3 ----------------an epoch starts------------------- i_epoch: 35 # batch: 96 i_batch: 0.0 the loss for this batch: 0.46442944 flow loss 0.1164882 occ loss 0.145547 time for this batch 0.45943784713745117 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.37692225 flow loss 0.08171541 occ loss 0.09934143 time for this batch 0.48022985458374023 ---------------------------------- train loss for this epoch: 0.38721
time for this epoch 56.67690563201904 No_decrease: 4 ----------------an epoch starts------------------- i_epoch: 36 # batch: 96 i_batch: 0.0 the loss for this batch: 0.3427189 flow loss 0.09798035 occ loss 0.0952055 time for this batch 0.4549384117126465 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.41036835 flow loss 0.11359605 occ loss 0.113046296 time for this batch 0.4846916198730469 ---------------------------------- train loss for this epoch: 0.38233
time for this epoch 57.439910888671875 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 37 # batch: 96 i_batch: 0.0 the loss for this batch: 0.38860953 flow loss 0.09790813 occ loss 0.099632695 time for this batch 0.4678535461425781 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.35990876 flow loss 0.08968501 occ loss 0.110446446 time for this batch 0.48055028915405273 ---------------------------------- train loss for this epoch: 0.368804
time for this epoch 55.91486930847168 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 38 # batch: 96 i_batch: 0.0 the loss for this batch: 0.33679757 flow loss 0.0888739 occ loss 0.09255929 time for this batch 0.4342324733734131 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.36533523 flow loss 0.09623773 occ loss 0.111288585 time for this batch 0.506514310836792 ---------------------------------- train loss for this epoch: 0.356988
time for this epoch 57.38023543357849 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 39 # batch: 96 i_batch: 0.0 the loss for this batch: 0.30428326 flow loss 0.081718005 occ loss 0.07787413 time for this batch 0.43451476097106934 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.35058337 flow loss 0.103774294 occ loss 0.101473264 time for this batch 0.48511266708374023 ---------------------------------- train loss for this epoch: 0.340387
time for this epoch 56.17021298408508 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 40 # batch: 96 i_batch: 0.0 the loss for this batch: 0.29709947 flow loss 0.06864549 occ loss 0.08598895 time for this batch 0.45409631729125977 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.330058 flow loss 0.07955239 occ loss 0.11494616 time for this batch 0.45813751220703125 ---------------------------------- train loss for this epoch: 0.323378
time for this epoch 54.42401075363159 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 41 # batch: 96 i_batch: 0.0 the loss for this batch: 0.30967814 flow loss 0.08537856 occ loss 0.10548884 time for this batch 0.4891228675842285 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.31138682 flow loss 0.08362573 occ loss 0.110224776 time for this batch 0.46434855461120605 ---------------------------------- train loss for this epoch: 0.299639
time for this epoch 57.17849898338318 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 42 # batch: 96 i_batch: 0.0 the loss for this batch: 0.2293435 flow loss 0.06885545 occ loss 0.090316094 time for this batch 0.4408726692199707 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.2549287 flow loss 0.07678229 occ loss 0.09380289 time for this batch 0.4701120853424072 ---------------------------------- train loss for this epoch: 0.276589
time for this epoch 56.65172004699707 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 43 # batch: 96 i_batch: 0.0 the loss for this batch: 0.26807967 flow loss 0.08296997 occ loss 0.10264808 time for this batch 0.4392688274383545 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.27827975 flow loss 0.085978046 occ loss 0.114176005 time for this batch 0.37773609161376953 ---------------------------------- train loss for this epoch: 0.250583
time for this epoch 55.67051911354065 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 44 # batch: 96 i_batch: 0.0 the loss for this batch: 0.28988692 flow loss 0.0847115 occ loss 0.1292841 time for this batch 0.46034717559814453 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.24618478 flow loss 0.07984559 occ loss 0.11801133 time for this batch 0.4771285057067871 ---------------------------------- train loss for this epoch: 0.22595
time for this epoch 57.171910524368286 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 45 # batch: 96 i_batch: 0.0 the loss for this batch: 0.19792622 flow loss 0.07039622 occ loss 0.08873201 time for this batch 0.43613314628601074 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.15931503 flow loss 0.061350692 occ loss 0.0752038 time for this batch 0.46597790718078613 ---------------------------------- train loss for this epoch: 0.205864
time for this epoch 57.172303199768066 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 46 # batch: 96 i_batch: 0.0 the loss for this batch: 0.17574093 flow loss 0.06466974 occ loss 0.093312494 time for this batch 0.3650803565979004 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.18421389 flow loss 0.06959682 occ loss 0.100628816 time for this batch 0.48705387115478516 ---------------------------------- train loss for this epoch: 0.191208
time for this epoch 56.51705741882324 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 47 # batch: 96 i_batch: 0.0 the loss for this batch: 0.19786349 flow loss 0.0684956 occ loss 0.11329075 time for this batch 0.44215822219848633 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.21038693 flow loss 0.0711813 occ loss 0.13042372 time for this batch 0.4729795455932617 ---------------------------------- train loss for this epoch: 0.182046
time for this epoch 56.669657468795776 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 48 # batch: 96 i_batch: 0.0 the loss for this batch: 0.16394475 flow loss 0.059930235 occ loss 0.096572615 time for this batch 0.47124266624450684 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.19114932 flow loss 0.073599055 occ loss 0.11209632 time for this batch 0.49654054641723633 ---------------------------------- train loss for this epoch: 0.174602
time for this epoch 57.09907126426697 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 49 # batch: 96 i_batch: 0.0 the loss for this batch: 0.17088449 flow loss 0.06740076 occ loss 0.09892282 time for this batch 0.41875648498535156 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.22656266 flow loss 0.077932075 occ loss 0.1461927 time for this batch 0.470184326171875 ---------------------------------- train loss for this epoch: 0.171534
time for this epoch 56.55620312690735 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 50 # batch: 96 i_batch: 0.0 the loss for this batch: 0.1428076 flow loss 0.060366612 occ loss 0.08066277 time for this batch 0.45896100997924805 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.2833389 flow loss 0.123044565 occ loss 0.15755492 time for this batch 0.45763111114501953 ---------------------------------- train loss for this epoch: 0.208377
time for this epoch 55.74706530570984 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 51 # batch: 96 i_batch: 0.0 the loss for this batch: 0.17089728 flow loss 0.075031966 occ loss 0.094032966 time for this batch 0.4688386917114258 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16287541 flow loss 0.06979747 occ loss 0.09188579 time for this batch 0.4555394649505615 ---------------------------------- train loss for this epoch: 0.181095
time for this epoch 55.33394145965576 No_decrease: 3 ----------------an epoch starts------------------- i_epoch: 52 # batch: 96 i_batch: 0.0 the loss for this batch: 0.14237644 flow loss 0.062444765 occ loss 0.078261144 time for this batch 0.42099666595458984 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.18765439 flow loss 0.07193016 occ loss 0.11448983 time for this batch 0.47904038429260254 ---------------------------------- train loss for this epoch: 0.173269
time for this epoch 57.21428155899048 No_decrease: 4 ----------------an epoch starts------------------- i_epoch: 53 # batch: 96 i_batch: 0.0 the loss for this batch: 0.14639401 flow loss 0.06078006 occ loss 0.08484088 time for this batch 0.4494805335998535 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16742866 flow loss 0.062452033 occ loss 0.10344128 time for this batch 0.5286417007446289 ---------------------------------- train loss for this epoch: 0.172894
time for this epoch 57.46303200721741 No_decrease: 5 ----------------an epoch starts------------------- i_epoch: 54 # batch: 96 i_batch: 0.0 the loss for this batch: 0.20473146 flow loss 0.0717112 occ loss 0.13184822 time for this batch 0.44265317916870117 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.18087377 flow loss 0.068759404 occ loss 0.11120617 time for this batch 0.5041193962097168 ---------------------------------- train loss for this epoch: 0.169849
time for this epoch 57.953922510147095 No_decrease: 6 ----------------an epoch starts------------------- i_epoch: 55 # batch: 96 i_batch: 0.0 the loss for this batch: 0.16900903 flow loss 0.06625625 occ loss 0.10207065 time for this batch 0.3349134922027588 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.19790557 flow loss 0.07253476 occ loss 0.12452545 time for this batch 0.480283260345459 ---------------------------------- train loss for this epoch: 0.168769
time for this epoch 57.62197780609131 No_decrease: 7 ----------------an epoch starts------------------- i_epoch: 56 # batch: 96 i_batch: 0.0 the loss for this batch: 0.16645174 flow loss 0.063629635 occ loss 0.10210735 time for this batch 0.4529547691345215 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.1650283 flow loss 0.06856293 occ loss 0.0951004 time for this batch 0.45896339416503906 ---------------------------------- train loss for this epoch: 0.168171
time for this epoch 56.59158539772034 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 57 # batch: 96 i_batch: 0.0 the loss for this batch: 0.16866548 flow loss 0.06445403 occ loss 0.10328936 time for this batch 0.45575690269470215 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16972452 flow loss 0.06953925 occ loss 0.0990324 time for this batch 0.49000048637390137 ---------------------------------- train loss for this epoch: 0.167159
time for this epoch 56.94491100311279 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 58 # batch: 96 i_batch: 0.0 the loss for this batch: 0.14326268 flow loss 0.057386473 occ loss 0.08495182 time for this batch 0.5034477710723877 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16092189 flow loss 0.06238699 occ loss 0.09764191 time for this batch 0.5142927169799805 ---------------------------------- train loss for this epoch: 0.16758
time for this epoch 59.13537669181824 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 59 # batch: 96 i_batch: 0.0 the loss for this batch: 0.17510428 flow loss 0.06604984 occ loss 0.10813903 time for this batch 0.48776984214782715 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.15720923 flow loss 0.06321975 occ loss 0.09241863 time for this batch 0.49738550186157227 ---------------------------------- train loss for this epoch: 0.167178
time for this epoch 57.84379601478577 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 60 # batch: 96 i_batch: 0.0 the loss for this batch: 0.14930384 flow loss 0.059290987 occ loss 0.089361094 time for this batch 0.46717262268066406 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16792078 flow loss 0.06330743 occ loss 0.10391903 time for this batch 0.5104665756225586 ---------------------------------- train loss for this epoch: 0.165691
time for this epoch 58.642645835876465 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 61 # batch: 96 i_batch: 0.0 the loss for this batch: 0.15876001 flow loss 0.06035288 occ loss 0.09768496 time for this batch 0.42600393295288086 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.20849803 flow loss 0.071591035 occ loss 0.13569331 time for this batch 0.5122506618499756 ---------------------------------- train loss for this epoch: 0.166259
time for this epoch 58.219282150268555 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 62 # batch: 96 i_batch: 0.0 the loss for this batch: 0.16295478 flow loss 0.06106617 occ loss 0.10129363 time for this batch 0.4499802589416504 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16171934 flow loss 0.0625629 occ loss 0.09839725 time for this batch 0.41702818870544434 ---------------------------------- train loss for this epoch: 0.165842
time for this epoch 58.26057410240173 No_decrease: 3 ----------------an epoch starts------------------- i_epoch: 63 # batch: 96 i_batch: 0.0 the loss for this batch: 0.16997434 flow loss 0.0623505 occ loss 0.106937416 time for this batch 0.4203989505767822 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.18153788 flow loss 0.065317035 occ loss 0.11526609 time for this batch 0.5060584545135498 ---------------------------------- train loss for this epoch: 0.165406
time for this epoch 56.74946308135986 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 64 # batch: 96 i_batch: 0.0 the loss for this batch: 0.184158 flow loss 0.063978314 occ loss 0.11917093 time for this batch 0.46250271797180176 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.14562623 flow loss 0.059979 occ loss 0.084549926 time for this batch 0.5010561943054199 ---------------------------------- train loss for this epoch: 0.164737
time for this epoch 57.00438165664673 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 65 # batch: 96 i_batch: 0.0 the loss for this batch: 0.12589265 flow loss 0.05377545 occ loss 0.07122317 time for this batch 0.4634981155395508 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.14692923 flow loss 0.060727924 occ loss 0.08504384 time for this batch 0.47271728515625 ---------------------------------- train loss for this epoch: 0.165114
time for this epoch 57.064369678497314 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 66 # batch: 96 i_batch: 0.0 the loss for this batch: 0.17846623 flow loss 0.062074244 occ loss 0.11580289 time for this batch 0.45317959785461426 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.18721661 flow loss 0.06966216 occ loss 0.11668047 time for this batch 0.42349839210510254 ---------------------------------- train loss for this epoch: 0.164326
time for this epoch 57.09487462043762 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 67 # batch: 96 i_batch: 0.0 the loss for this batch: 0.15935868 flow loss 0.062124245 occ loss 0.09650708 time for this batch 0.4172835350036621 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.17151137 flow loss 0.066757984 occ loss 0.10382885 time for this batch 0.35741090774536133 ---------------------------------- train loss for this epoch: 0.164416
time for this epoch 56.38816690444946 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 68 # batch: 96 i_batch: 0.0 the loss for this batch: 0.17448127 flow loss 0.06358678 occ loss 0.10960779 time for this batch 0.4515829086303711 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16099973 flow loss 0.0632524 occ loss 0.09660716 time for this batch 0.4829061031341553 ---------------------------------- train loss for this epoch: 0.163123
time for this epoch 57.00096917152405 No_decrease: 3 ----------------an epoch starts------------------- i_epoch: 69 # batch: 96 i_batch: 0.0 the loss for this batch: 0.16798338 flow loss 0.063675605 occ loss 0.10315779 time for this batch 0.4755103588104248 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.18213612 flow loss 0.06802641 occ loss 0.11329841 time for this batch 0.511145830154419 ---------------------------------- train loss for this epoch: 0.163997
time for this epoch 57.557461738586426 No_decrease: 4 ----------------an epoch starts------------------- i_epoch: 70 # batch: 96 i_batch: 0.0 the loss for this batch: 0.16620997 flow loss 0.060995094 occ loss 0.10417847 time for this batch 0.35910534858703613 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.20417729 flow loss 0.06878644 occ loss 0.13472597 time for this batch 0.48972034454345703 ---------------------------------- train loss for this epoch: 0.165252
time for this epoch 56.93786334991455 No_decrease: 5 ----------------an epoch starts------------------- i_epoch: 71 # batch: 96 i_batch: 0.0 the loss for this batch: 0.18956366 flow loss 0.06561856 occ loss 0.12293041 time for this batch 0.4202275276184082 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.14005043 flow loss 0.05528647 occ loss 0.08383574 time for this batch 0.5098414421081543 ---------------------------------- train loss for this epoch: 0.164065
time for this epoch 59.500913858413696 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 72 # batch: 96 i_batch: 0.0 the loss for this batch: 0.1651399 flow loss 0.06292015 occ loss 0.101597354 time for this batch 0.46848058700561523 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16473489 flow loss 0.06019748 occ loss 0.103829235 time for this batch 0.4980447292327881 ---------------------------------- train loss for this epoch: 0.162236
time for this epoch 58.979766845703125 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 73 # batch: 96 i_batch: 0.0 the loss for this batch: 0.1551562 flow loss 0.060430728 occ loss 0.09381386 time for this batch 0.4366271495819092 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.207159 flow loss 0.06718065 occ loss 0.13920923 time for this batch 0.4886767864227295 ---------------------------------- train loss for this epoch: 0.162877
time for this epoch 56.99981331825256 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 74 # batch: 96 i_batch: 0.0 the loss for this batch: 0.15198943 flow loss 0.057112116 occ loss 0.093876354 time for this batch 0.3632652759552002 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.14739034 flow loss 0.05534512 occ loss 0.091243535 time for this batch 0.49224185943603516 ---------------------------------- train loss for this epoch: 0.161518
time for this epoch 55.73976111412048 No_decrease: 3 ----------------an epoch starts------------------- i_epoch: 75 # batch: 96 i_batch: 0.0 the loss for this batch: 0.16856128 flow loss 0.06406727 occ loss 0.10374503 time for this batch 0.45081400871276855 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.1517569 flow loss 0.06090799 occ loss 0.090283126 time for this batch 0.4971909523010254 ---------------------------------- train loss for this epoch: 0.162308
time for this epoch 58.051241874694824 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 76 # batch: 96 i_batch: 0.0 the loss for this batch: 0.14166099 flow loss 0.06083123 occ loss 0.080026254 time for this batch 0.4494476318359375 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.1677501 flow loss 0.0614664 occ loss 0.10518044 time for this batch 0.4883701801300049 ---------------------------------- train loss for this epoch: 0.16361
time for this epoch 57.99287986755371 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 77 # batch: 96 i_batch: 0.0 the loss for this batch: 0.15977444 flow loss 0.062656164 occ loss 0.09638435 time for this batch 0.45237016677856445 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.092287436 flow loss 0.044855747 occ loss 0.046836227 time for this batch 0.4672055244445801 ---------------------------------- train loss for this epoch: 0.161159
time for this epoch 57.1151123046875 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 78 # batch: 96 i_batch: 0.0 the loss for this batch: 0.17100927 flow loss 0.06293436 occ loss 0.1071711 time for this batch 0.46407389640808105 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.2179169 flow loss 0.09177256 occ loss 0.12501116 time for this batch 0.482928991317749 ---------------------------------- train loss for this epoch: 0.168809
time for this epoch 57.661136627197266 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 79 # batch: 96 i_batch: 0.0 the loss for this batch: 0.16424689 flow loss 0.06634416 occ loss 0.09712871 time for this batch 0.414722204208374 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.1828493 flow loss 0.066890806 occ loss 0.11492169 time for this batch 0.4996683597564697 ---------------------------------- train loss for this epoch: 0.163822
time for this epoch 58.307910680770874 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 80 # batch: 96 i_batch: 0.0 the loss for this batch: 0.14813781 flow loss 0.056760103 occ loss 0.09057009 time for this batch 0.4371829032897949 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.14118828 flow loss 0.05832357 occ loss 0.08185099 time for this batch 0.48531460762023926 ---------------------------------- train loss for this epoch: 0.160934
time for this epoch 57.48443150520325 No_decrease: 3 ----------------an epoch starts------------------- i_epoch: 81 # batch: 96 i_batch: 0.0 the loss for this batch: 0.15444846 flow loss 0.058809947 occ loss 0.09470076 time for this batch 0.442751407623291 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.13921529 flow loss 0.057484493 occ loss 0.0806709 time for this batch 0.4849681854248047 ---------------------------------- train loss for this epoch: 0.160389
time for this epoch 56.83347702026367 No_decrease: 4 ----------------an epoch starts------------------- i_epoch: 82 # batch: 96 i_batch: 0.0 the loss for this batch: 0.15157185 flow loss 0.059637077 occ loss 0.09069639 time for this batch 0.3979012966156006 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.12656179 flow loss 0.054665733 occ loss 0.07066437 time for this batch 0.4450836181640625 ---------------------------------- train loss for this epoch: 0.160942
time for this epoch 56.5713529586792 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 83 # batch: 96 i_batch: 0.0 the loss for this batch: 0.16451642 flow loss 0.061535735 occ loss 0.10234887 time for this batch 0.44217371940612793 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.10741943 flow loss 0.047859915 occ loss 0.059002567 time for this batch 0.4642512798309326 ---------------------------------- train loss for this epoch: 0.161651
time for this epoch 56.25226044654846 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 84 # batch: 96 i_batch: 0.0 the loss for this batch: 0.14487855 flow loss 0.0543521 occ loss 0.089553796 time for this batch 0.45827651023864746 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.18278173 flow loss 0.06400986 occ loss 0.11776061 time for this batch 0.47780656814575195 ---------------------------------- train loss for this epoch: 0.159069
time for this epoch 56.585936307907104 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 85 # batch: 96 i_batch: 0.0 the loss for this batch: 0.10593353 flow loss 0.049458157 occ loss 0.055602342 time for this batch 0.430591344833374 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.124664426 flow loss 0.05237726 occ loss 0.07138876 time for this batch 0.5040795803070068 ---------------------------------- train loss for this epoch: 0.15894
time for this epoch 57.932048320770264 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 86 # batch: 96 i_batch: 0.0 the loss for this batch: 0.14608681 flow loss 0.05721428 occ loss 0.08817356 time for this batch 0.34967875480651855 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.19725312 flow loss 0.066106945 occ loss 0.13049625 time for this batch 0.5066368579864502 ---------------------------------- train loss for this epoch: 0.160667
time for this epoch 57.198086738586426 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 87 # batch: 96 i_batch: 0.0 the loss for this batch: 0.12624809 flow loss 0.05434687 occ loss 0.07093294 time for this batch 0.469038724899292 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.15927063 flow loss 0.05976685 occ loss 0.09852124 time for this batch 0.47913098335266113 ---------------------------------- train loss for this epoch: 0.158973
time for this epoch 57.24359154701233 No_decrease: 3 ----------------an epoch starts------------------- i_epoch: 88 # batch: 96 i_batch: 0.0 the loss for this batch: 0.13549732 flow loss 0.055640582 occ loss 0.0788288 time for this batch 0.4405100345611572 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.14300615 flow loss 0.0586505 occ loss 0.08380779 time for this batch 0.3932919502258301 ---------------------------------- train loss for this epoch: 0.159793
time for this epoch 56.046892166137695 No_decrease: 4 ----------------an epoch starts------------------- i_epoch: 89 # batch: 96 i_batch: 0.0 the loss for this batch: 0.13263316 flow loss 0.052975234 occ loss 0.07854462 time for this batch 0.44124794006347656 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.1867388 flow loss 0.06747816 occ loss 0.11828478 time for this batch 0.5048551559448242 ---------------------------------- train loss for this epoch: 0.158638
time for this epoch 57.45936417579651 No_decrease: 5 ----------------an epoch starts------------------- i_epoch: 90 # batch: 96 i_batch: 0.0 the loss for this batch: 0.15023059 flow loss 0.056648728 occ loss 0.09276998 time for this batch 0.45366525650024414 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16531779 flow loss 0.05965275 occ loss 0.10492268 time for this batch 0.44420480728149414 ---------------------------------- train loss for this epoch: 0.159817
time for this epoch 57.07018709182739 No_decrease: 6 ----------------an epoch starts------------------- i_epoch: 91 # batch: 96 i_batch: 0.0 the loss for this batch: 0.18540736 flow loss 0.063485645 occ loss 0.12131392 time for this batch 0.47290992736816406 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.13484168 flow loss 0.05363951 occ loss 0.07993434 time for this batch 0.502465009689331 ---------------------------------- train loss for this epoch: 0.158088
time for this epoch 56.84256863594055 No_decrease: 7 ----------------an epoch starts------------------- i_epoch: 92 # batch: 96 i_batch: 0.0 the loss for this batch: 0.16546276 flow loss 0.062683776 occ loss 0.101810515 time for this batch 0.4441978931427002 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.15678002 flow loss 0.057858046 occ loss 0.098220915 time for this batch 0.4653968811035156 ---------------------------------- train loss for this epoch: 0.159379
time for this epoch 56.27992343902588 No_decrease: 8 ----------------an epoch starts------------------- i_epoch: 93 # batch: 96 i_batch: 0.0 the loss for this batch: 0.17639624 flow loss 0.06340785 occ loss 0.11230188 time for this batch 0.36490368843078613 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.15846021 flow loss 0.058906607 occ loss 0.098815754 time for this batch 0.5048661231994629 ---------------------------------- train loss for this epoch: 0.158723
time for this epoch 56.312355518341064 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 94 # batch: 96 i_batch: 0.0 the loss for this batch: 0.15859027 flow loss 0.054821394 occ loss 0.10313759 time for this batch 0.43781399726867676 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.18301748 flow loss 0.067438476 occ loss 0.114566855 time for this batch 0.5034055709838867 ---------------------------------- train loss for this epoch: 0.157611
time for this epoch 56.5135555267334 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 95 # batch: 96 i_batch: 0.0 the loss for this batch: 0.15965842 flow loss 0.061167754 occ loss 0.09772313 time for this batch 0.4513366222381592 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.14544384 flow loss 0.058060247 occ loss 0.08635304 time for this batch 0.5201408863067627 ---------------------------------- train loss for this epoch: 0.158023
time for this epoch 57.919347286224365 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 96 # batch: 96 i_batch: 0.0 the loss for this batch: 0.16567686 flow loss 0.062200494 occ loss 0.102673545 time for this batch 0.43378376960754395 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.1687215 flow loss 0.06049126 occ loss 0.107740685 time for this batch 0.43665575981140137 ---------------------------------- train loss for this epoch: 0.158017
time for this epoch 57.63165354728699 No_decrease: 3 ----------------an epoch starts------------------- i_epoch: 97 # batch: 96 i_batch: 0.0 the loss for this batch: 0.1518107 flow loss 0.05500412 occ loss 0.09583255 time for this batch 0.4722757339477539 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.18757036 flow loss 0.0652224 occ loss 0.121607244 time for this batch 0.3050501346588135 ---------------------------------- train loss for this epoch: 0.157322
time for this epoch 57.86514949798584 No_decrease: 4 ----------------an epoch starts------------------- i_epoch: 98 # batch: 96 i_batch: 0.0 the loss for this batch: 0.14076038 flow loss 0.057656985 occ loss 0.08228671 time for this batch 0.4721841812133789 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.15276384 flow loss 0.059886385 occ loss 0.09195231 time for this batch 0.4973745346069336 ---------------------------------- train loss for this epoch: 0.157356
time for this epoch 56.73479509353638 No_decrease: 5 ----------------an epoch starts------------------- i_epoch: 99 # batch: 96 i_batch: 0.0 the loss for this batch: 0.19427268 flow loss 0.06582773 occ loss 0.12770353 time for this batch 0.4646031856536865 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.14351463 flow loss 0.05867084 occ loss 0.0835708 time for this batch 0.4566919803619385 ---------------------------------- train loss for this epoch: 0.15735
time for this epoch 57.091628074645996 No_decrease: 6 ----------------an epoch starts------------------- i_epoch: 100 # batch: 96 i_batch: 0.0 the loss for this batch: 0.14916697 flow loss 0.059164964 occ loss 0.08928162 time for this batch 0.44809699058532715 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.15047716 flow loss 0.05956196 occ loss 0.090429395 time for this batch 0.4491119384765625 ---------------------------------- train loss for this epoch: 0.156982
time for this epoch 58.54954552650452 No_decrease: 7 ----------------an epoch starts------------------- i_epoch: 101 # batch: 96 i_batch: 0.0 the loss for this batch: 0.13235846 flow loss 0.053304307 occ loss 0.07808786 time for this batch 0.46100521087646484 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.14960107 flow loss 0.054277085 occ loss 0.09466111 time for this batch 0.45705246925354004 ---------------------------------- train loss for this epoch: 0.156617
time for this epoch 56.588552951812744 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 102 # batch: 96 i_batch: 0.0 the loss for this batch: 0.13999432 flow loss 0.056790978 occ loss 0.08211697 time for this batch 0.5053050518035889 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16723812 flow loss 0.060332436 occ loss 0.106049225 time for this batch 0.48012351989746094 ---------------------------------- train loss for this epoch: 0.156008
time for this epoch 57.66069173812866 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 103 # batch: 96 i_batch: 0.0 the loss for this batch: 0.16420594 flow loss 0.058806907 occ loss 0.104724884 time for this batch 0.4810056686401367 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.14653224 flow loss 0.05666993 occ loss 0.08887997 time for this batch 0.4731252193450928 ---------------------------------- train loss for this epoch: 0.156305
time for this epoch 56.271113872528076 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 104 # batch: 96 i_batch: 0.0 the loss for this batch: 0.16534367 flow loss 0.060000654 occ loss 0.10454603 time for this batch 0.4500772953033447 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.13078958 flow loss 0.055756483 occ loss 0.07408799 time for this batch 0.4649341106414795 ---------------------------------- train loss for this epoch: 0.157884
time for this epoch 56.26455640792847 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 105 # batch: 96 i_batch: 0.0 the loss for this batch: 0.18537314 flow loss 0.07287472 occ loss 0.11114914 time for this batch 0.4620978832244873 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.17300302 flow loss 0.06076697 occ loss 0.11144735 time for this batch 0.48777008056640625 ---------------------------------- train loss for this epoch: 0.157914
time for this epoch 56.550880432128906 No_decrease: 3 ----------------an epoch starts------------------- i_epoch: 106 # batch: 96 i_batch: 0.0 the loss for this batch: 0.1861492 flow loss 0.064636566 occ loss 0.12089533 time for this batch 0.43120908737182617 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.13659805 flow loss 0.053865224 occ loss 0.081748426 time for this batch 0.5223298072814941 ---------------------------------- train loss for this epoch: 0.155441
time for this epoch 59.562175273895264 No_decrease: 4 ----------------an epoch starts------------------- i_epoch: 107 # batch: 96 i_batch: 0.0 the loss for this batch: 0.11833981 flow loss 0.051789228 occ loss 0.06593726 time for this batch 0.4761042594909668 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.17321506 flow loss 0.061268013 occ loss 0.11087955 time for this batch 0.48718762397766113 ---------------------------------- train loss for this epoch: 0.157239
time for this epoch 58.64956545829773 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 108 # batch: 96 i_batch: 0.0 the loss for this batch: 0.12507223 flow loss 0.048974585 occ loss 0.07538375 time for this batch 0.5051686763763428 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16668752 flow loss 0.058186397 occ loss 0.10770346 time for this batch 0.5522987842559814 ---------------------------------- train loss for this epoch: 0.155205
time for this epoch 58.41923499107361 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 109 # batch: 96 i_batch: 0.0 the loss for this batch: 0.18447877 flow loss 0.06396446 occ loss 0.11945831 time for this batch 0.44652557373046875 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.14049836 flow loss 0.054446436 occ loss 0.08503991 time for this batch 0.3970625400543213 ---------------------------------- train loss for this epoch: 0.156353
time for this epoch 57.54139423370361 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 110 # batch: 96 i_batch: 0.0 the loss for this batch: 0.19483674 flow loss 0.0669508 occ loss 0.12665315 time for this batch 0.44986391067504883 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.11170816 flow loss 0.044999026 occ loss 0.0657573 time for this batch 0.48705339431762695 ---------------------------------- train loss for this epoch: 0.158306
time for this epoch 58.80824303627014 No_decrease: 3 ----------------an epoch starts------------------- i_epoch: 111 # batch: 96 i_batch: 0.0 the loss for this batch: 0.16938204 flow loss 0.05848166 occ loss 0.110217355 time for this batch 0.4786968231201172 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16671339 flow loss 0.058077022 occ loss 0.107549034 time for this batch 0.4833977222442627 ---------------------------------- train loss for this epoch: 0.15468
time for this epoch 57.45481324195862 No_decrease: 4 ----------------an epoch starts------------------- i_epoch: 112 # batch: 96 i_batch: 0.0 the loss for this batch: 0.18923552 flow loss 0.06556541 occ loss 0.12292136 time for this batch 0.41150665283203125 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16858889 flow loss 0.061505552 occ loss 0.10637223 time for this batch 0.4665720462799072 ---------------------------------- train loss for this epoch: 0.153924
time for this epoch 57.80531930923462 No_decrease: 5 ----------------an epoch starts------------------- i_epoch: 113 # batch: 96 i_batch: 0.0 the loss for this batch: 0.1672737 flow loss 0.059145715 occ loss 0.10719332 time for this batch 0.47563672065734863 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.14798276 flow loss 0.056332294 occ loss 0.09100913 time for this batch 0.41141295433044434 ---------------------------------- train loss for this epoch: 0.155564
time for this epoch 48.02052187919617 No_decrease: 6 ----------------an epoch starts------------------- i_epoch: 114 # batch: 96 i_batch: 0.0 the loss for this batch: 0.13051385 flow loss 0.05159614 occ loss 0.07752693 time for this batch 0.3946261405944824 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.13498458 flow loss 0.054472137 occ loss 0.079710595 time for this batch 0.44052839279174805 ---------------------------------- train loss for this epoch: 0.154688
time for this epoch 47.87200570106506 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 115 # batch: 96 i_batch: 0.0 the loss for this batch: 0.16468288 flow loss 0.05823496 occ loss 0.10580814 time for this batch 0.39049363136291504 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.18791206 flow loss 0.06419406 occ loss 0.12313473 time for this batch 0.41671061515808105 ---------------------------------- train loss for this epoch: 0.155422
time for this epoch 48.83049559593201 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 116 # batch: 96 i_batch: 0.0 the loss for this batch: 0.15067792 flow loss 0.05792886 occ loss 0.09161641 time for this batch 0.37308216094970703 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.17988585 flow loss 0.06459574 occ loss 0.11446693 time for this batch 0.40114688873291016 ---------------------------------- train loss for this epoch: 0.156041
time for this epoch 46.91168189048767 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 117 # batch: 96 i_batch: 0.0 the loss for this batch: 0.13867481 flow loss 0.057609774 occ loss 0.08007535 time for this batch 0.3638455867767334 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16317712 flow loss 0.05741386 occ loss 0.1048155 time for this batch 0.42548418045043945 ---------------------------------- train loss for this epoch: 0.155502
time for this epoch 50.67264676094055 No_decrease: 3 ----------------an epoch starts------------------- i_epoch: 118 # batch: 96 i_batch: 0.0 the loss for this batch: 0.1370633 flow loss 0.051552564 occ loss 0.08465434 time for this batch 0.3566768169403076 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.19451173 flow loss 0.063642986 occ loss 0.13010818 time for this batch 0.4012455940246582 ---------------------------------- train loss for this epoch: 0.154724
time for this epoch 47.07816457748413 No_decrease: 4 ----------------an epoch starts------------------- i_epoch: 119 # batch: 96 i_batch: 0.0 the loss for this batch: 0.15026748 flow loss 0.05623838 occ loss 0.093110345 time for this batch 0.366635799407959 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.15122256 flow loss 0.05905317 occ loss 0.09123556 time for this batch 0.39893579483032227 ---------------------------------- train loss for this epoch: 0.154577
time for this epoch 48.368045806884766 No_decrease: 5 ----------------an epoch starts------------------- i_epoch: 120 # batch: 96 i_batch: 0.0 the loss for this batch: 0.13345711 flow loss 0.05163934 occ loss 0.081160046 time for this batch 0.36498022079467773 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.18433917 flow loss 0.0640707 occ loss 0.119453646 time for this batch 0.40045666694641113 ---------------------------------- train loss for this epoch: 0.153547
time for this epoch 46.65022826194763 No_decrease: 6 ----------------an epoch starts------------------- i_epoch: 121 # batch: 96 i_batch: 0.0 the loss for this batch: 0.17450094 flow loss 0.05972431 occ loss 0.11416137 time for this batch 0.4234917163848877 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.17500958 flow loss 0.0629132 occ loss 0.1112012 time for this batch 0.41067075729370117 ---------------------------------- train loss for this epoch: 0.154484
time for this epoch 50.49652624130249 No_decrease: 7 ----------------an epoch starts------------------- i_epoch: 122 # batch: 96 i_batch: 0.0 the loss for this batch: 0.15451689 flow loss 0.05552986 occ loss 0.098250516 time for this batch 0.3132059574127197 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.11892879 flow loss 0.0514266 occ loss 0.06645061 time for this batch 0.3972194194793701 ---------------------------------- train loss for this epoch: 0.153562
time for this epoch 48.31332278251648 No_decrease: 8 ----------------an epoch starts------------------- i_epoch: 123 # batch: 96 i_batch: 0.0 the loss for this batch: 0.15302289 flow loss 0.055392712 occ loss 0.09673949 time for this batch 0.40219902992248535 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.14260595 flow loss 0.05883269 occ loss 0.083043784 time for this batch 0.43140339851379395 ---------------------------------- train loss for this epoch: 0.154649
time for this epoch 47.95385146141052 No_decrease: 9 ----------------an epoch starts------------------- i_epoch: 124 # batch: 96 i_batch: 0.0 the loss for this batch: 0.15461752 flow loss 0.05771293 occ loss 0.09592319 time for this batch 0.3458707332611084 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.14006919 flow loss 0.05545568 occ loss 0.083789505 time for this batch 0.3985593318939209 ---------------------------------- train loss for this epoch: 0.15497
time for this epoch 49.04661011695862 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 125 # batch: 96 i_batch: 0.0 the loss for this batch: 0.15748465 flow loss 0.057813253 occ loss 0.09860149 time for this batch 0.3582022190093994 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.15221055 flow loss 0.055622946 occ loss 0.09607126 time for this batch 0.4089348316192627 ---------------------------------- train loss for this epoch: 0.152565
time for this epoch 46.71931433677673 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 126 # batch: 96 i_batch: 0.0 the loss for this batch: 0.18489592 flow loss 0.06211215 occ loss 0.12202269 time for this batch 0.35335826873779297 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.122199915 flow loss 0.051151782 occ loss 0.070308276 time for this batch 0.43031764030456543 ---------------------------------- train loss for this epoch: 0.152527
time for this epoch 49.8864963054657 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 127 # batch: 96 i_batch: 0.0 the loss for this batch: 0.13992766 flow loss 0.05373134 occ loss 0.085244864 time for this batch 0.38612794876098633 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.1658553 flow loss 0.06242656 occ loss 0.10252008 time for this batch 0.4279508590698242 ---------------------------------- train loss for this epoch: 0.152255
time for this epoch 45.70396280288696 No_decrease: 3 ----------------an epoch starts------------------- i_epoch: 128 # batch: 96 i_batch: 0.0 the loss for this batch: 0.17398769 flow loss 0.060856543 occ loss 0.11241765 time for this batch 0.35540246963500977 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.14206694 flow loss 0.054703098 occ loss 0.08631265 time for this batch 0.3490581512451172 ---------------------------------- train loss for this epoch: 0.153187
time for this epoch 45.965306758880615 No_decrease: 4 ----------------an epoch starts------------------- i_epoch: 129 # batch: 96 i_batch: 0.0 the loss for this batch: 0.12457954 flow loss 0.04966625 occ loss 0.074011214 time for this batch 0.34937119483947754 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.13560843 flow loss 0.05084146 occ loss 0.08419414 time for this batch 0.4161250591278076 ---------------------------------- train loss for this epoch: 0.152419
time for this epoch 44.744980335235596 No_decrease: 5 ----------------an epoch starts------------------- i_epoch: 130 # batch: 96 i_batch: 0.0 the loss for this batch: 0.15702778 flow loss 0.06003109 occ loss 0.096167475 time for this batch 0.36261868476867676 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.20105645 flow loss 0.06595293 occ loss 0.13424434 time for this batch 0.4024038314819336 ---------------------------------- train loss for this epoch: 0.154265
time for this epoch 46.581308364868164 No_decrease: 6 ----------------an epoch starts------------------- i_epoch: 131 # batch: 96 i_batch: 0.0 the loss for this batch: 0.1572772 flow loss 0.053096224 occ loss 0.10362933 time for this batch 0.3386714458465576 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.13900036 flow loss 0.052440263 occ loss 0.08561021 time for this batch 0.4156210422515869 ---------------------------------- train loss for this epoch: 0.152297
time for this epoch 45.91918873786926 No_decrease: 7 ----------------an epoch starts------------------- i_epoch: 132 # batch: 96 i_batch: 0.0 the loss for this batch: 0.14202343 flow loss 0.05491642 occ loss 0.086413376 time for this batch 0.3616452217102051 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.12497675 flow loss 0.052797098 occ loss 0.07155668 time for this batch 0.42595505714416504 ---------------------------------- train loss for this epoch: 0.153291
time for this epoch 47.5648033618927 No_decrease: 8 ----------------an epoch starts------------------- i_epoch: 133 # batch: 96 i_batch: 0.0 the loss for this batch: 0.14338739 flow loss 0.056214828 occ loss 0.08633478 time for this batch 0.38284969329833984 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16993776 flow loss 0.058323853 occ loss 0.11054952 time for this batch 0.4487266540527344 ---------------------------------- train loss for this epoch: 0.152114
time for this epoch 49.170897006988525 No_decrease: 9 ----------------an epoch starts------------------- i_epoch: 134 # batch: 96 i_batch: 0.0 the loss for this batch: 0.11836463 flow loss 0.048329353 occ loss 0.069274575 time for this batch 0.40041589736938477 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.15290795 flow loss 0.059467107 occ loss 0.092814535 time for this batch 0.4528694152832031 ---------------------------------- train loss for this epoch: 0.150985
time for this epoch 48.54303431510925 No_decrease: 10 ----------------an epoch starts------------------- i_epoch: 135 # batch: 96 i_batch: 0.0 the loss for this batch: 0.13076098 flow loss 0.052908782 occ loss 0.07704843 time for this batch 0.3589451313018799 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.14984743 flow loss 0.054136496 occ loss 0.09492986 time for this batch 0.40830445289611816 ---------------------------------- train loss for this epoch: 0.151186
time for this epoch 47.210108041763306 No_decrease: 11 ----------------an epoch starts------------------- i_epoch: 136 # batch: 96 i_batch: 0.0 the loss for this batch: 0.14574954 flow loss 0.055200055 occ loss 0.08991081 time for this batch 0.36376523971557617 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.14664263 flow loss 0.05309029 occ loss 0.09279165 time for this batch 0.45195460319519043 ---------------------------------- train loss for this epoch: 0.152535
time for this epoch 48.105342388153076 No_decrease: 12 ----------------an epoch starts------------------- i_epoch: 137 # batch: 96 i_batch: 0.0 the loss for this batch: 0.1635921 flow loss 0.060272995 occ loss 0.10248759 time for this batch 0.36197519302368164 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16647352 flow loss 0.06292703 occ loss 0.10239473 time for this batch 0.4113931655883789 ---------------------------------- train loss for this epoch: 0.151819
time for this epoch 47.677505016326904 No_decrease: 13 ----------------an epoch starts------------------- i_epoch: 138 # batch: 96 i_batch: 0.0 the loss for this batch: 0.12628213 flow loss 0.052067984 occ loss 0.073415056 time for this batch 0.3546481132507324 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.13715728 flow loss 0.05555606 occ loss 0.08074377 time for this batch 0.3591015338897705 ---------------------------------- train loss for this epoch: 0.151063
time for this epoch 47.3203341960907 No_decrease: 14 ----------------an epoch starts------------------- i_epoch: 139 # batch: 96 i_batch: 0.0 the loss for this batch: 0.17992924 flow loss 0.06535856 occ loss 0.11373387 time for this batch 0.336489200592041 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.15050007 flow loss 0.05669841 occ loss 0.09291061 time for this batch 0.40776491165161133 ---------------------------------- train loss for this epoch: 0.151929
time for this epoch 47.597182512283325 No_decrease: 15 ----------------an epoch starts------------------- i_epoch: 140 # batch: 96 i_batch: 0.0 the loss for this batch: 0.13952245 flow loss 0.053656038 occ loss 0.084810324 time for this batch 0.3903827667236328 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.13923351 flow loss 0.050905883 occ loss 0.0874183 time for this batch 0.4213593006134033 ---------------------------------- train loss for this epoch: 0.151608
time for this epoch 47.09646940231323 No_decrease: 16 ----------------an epoch starts------------------- i_epoch: 141 # batch: 96 i_batch: 0.0 the loss for this batch: 0.18668158 flow loss 0.063786834 occ loss 0.12200097 time for this batch 0.35979223251342773 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.10565785 flow loss 0.04816964 occ loss 0.056404248 time for this batch 0.40865182876586914 ---------------------------------- train loss for this epoch: 0.156859
time for this epoch 48.576007604599 No_decrease: 17 ----------------an epoch starts------------------- i_epoch: 142 # batch: 96 i_batch: 0.0 the loss for this batch: 0.14935142 flow loss 0.05667464 occ loss 0.09209566 time for this batch 0.33305788040161133 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.19619286 flow loss 0.065082006 occ loss 0.13027397 time for this batch 0.41574692726135254 ---------------------------------- train loss for this epoch: 0.150647
time for this epoch 45.6798152923584 No_decrease: 18 ----------------an epoch starts------------------- i_epoch: 143 # batch: 96 i_batch: 0.0 the loss for this batch: 0.14176215 flow loss 0.057895783 occ loss 0.08273258 time for this batch 0.3360171318054199 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.14100383 flow loss 0.05324586 occ loss 0.08659945 time for this batch 0.38860511779785156 ---------------------------------- train loss for this epoch: 0.150796
time for this epoch 44.48101782798767 No_decrease: 19 ----------------an epoch starts------------------- i_epoch: 144 # batch: 96 i_batch: 0.0 the loss for this batch: 0.14681338 flow loss 0.053628393 occ loss 0.09213174 time for this batch 0.351473331451416 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.17259312 flow loss 0.059931103 occ loss 0.111193374 time for this batch 0.42259907722473145 ---------------------------------- train loss for this epoch: 0.153897
time for this epoch 46.422839641571045 No_decrease: 20 ----------------an epoch starts------------------- i_epoch: 145 # batch: 96 i_batch: 0.0 the loss for this batch: 0.15816486 flow loss 0.05908388 occ loss 0.0985028 time for this batch 0.35427021980285645 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.17481837 flow loss 0.05901495 occ loss 0.11498694 time for this batch 0.4056968688964844 ---------------------------------- train loss for this epoch: 0.151348
time for this epoch 45.05233645439148 No_decrease: 21 ----------------an epoch starts------------------- i_epoch: 146 # batch: 96 i_batch: 0.0 the loss for this batch: 0.17571689 flow loss 0.06151125 occ loss 0.113386564 time for this batch 0.32569384574890137 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.15512396 flow loss 0.0570137 occ loss 0.0973755 time for this batch 0.39374232292175293 ---------------------------------- train loss for this epoch: 0.150965
time for this epoch 44.44206976890564 No_decrease: 22 ----------------an epoch starts------------------- i_epoch: 147 # batch: 96 i_batch: 0.0 the loss for this batch: 0.13918348 flow loss 0.051807597 occ loss 0.08627266 time for this batch 0.3408956527709961 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16482882 flow loss 0.05910404 occ loss 0.10498573 time for this batch 0.3984107971191406 ---------------------------------- train loss for this epoch: 0.1506
time for this epoch 44.464545011520386 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 148 # batch: 96 i_batch: 0.0 the loss for this batch: 0.15252568 flow loss 0.056423515 occ loss 0.0955497 time for this batch 0.31807661056518555 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.1380129 flow loss 0.05316061 occ loss 0.084080644 time for this batch 0.34289121627807617 ---------------------------------- train loss for this epoch: 0.151224
time for this epoch 44.49526119232178 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 149 # batch: 96 i_batch: 0.0 the loss for this batch: 0.15712528 flow loss 0.056774132 occ loss 0.09951973 time for this batch 0.37717747688293457 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.11470985 flow loss 0.04801199 occ loss 0.065984786 time for this batch 0.3366830348968506 ---------------------------------- train loss for this epoch: 0.150286
time for this epoch 42.432223320007324 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 150 # batch: 96 i_batch: 0.0 the loss for this batch: 0.13525255 flow loss 0.05034166 occ loss 0.083330154 time for this batch 0.3041973114013672 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16000932 flow loss 0.055902027 occ loss 0.103501864 time for this batch 0.40467023849487305 ---------------------------------- train loss for this epoch: 0.14399
time for this epoch 43.29772448539734 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 151 # batch: 96 i_batch: 0.0 the loss for this batch: 0.119115435 flow loss 0.046671107 occ loss 0.07155427 time for this batch 0.2807340621948242 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.17236264 flow loss 0.059009295 occ loss 0.11287566 time for this batch 0.4089009761810303 ---------------------------------- train loss for this epoch: 0.142861
time for this epoch 44.51203727722168 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 152 # batch: 96 i_batch: 0.0 the loss for this batch: 0.14487104 flow loss 0.052959364 occ loss 0.091104075 time for this batch 0.318615198135376 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16569407 flow loss 0.057993315 occ loss 0.10679295 time for this batch 0.402576208114624 ---------------------------------- train loss for this epoch: 0.142624
time for this epoch 46.476269006729126 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 153 # batch: 96 i_batch: 0.0 the loss for this batch: 0.1419286 flow loss 0.0541011 occ loss 0.087140664 time for this batch 0.41809701919555664 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.14647597 flow loss 0.053992882 occ loss 0.09161444 time for this batch 0.4165318012237549 ---------------------------------- train loss for this epoch: 0.142283
time for this epoch 45.97362184524536 No_decrease: 3 ----------------an epoch starts------------------- i_epoch: 154 # batch: 96 i_batch: 0.0 the loss for this batch: 0.14990947 flow loss 0.056749374 occ loss 0.092292994 time for this batch 0.3493173122406006 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.13403353 flow loss 0.050208155 occ loss 0.082989015 time for this batch 0.4082024097442627 ---------------------------------- train loss for this epoch: 0.142172
time for this epoch 44.10191226005554 No_decrease: 4 ----------------an epoch starts------------------- i_epoch: 155 # batch: 96 i_batch: 0.0 the loss for this batch: 0.15056187 flow loss 0.053331647 occ loss 0.09648338 time for this batch 0.3430440425872803 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.119852066 flow loss 0.043884348 occ loss 0.07532722 time for this batch 0.37058568000793457 ---------------------------------- train loss for this epoch: 0.142788
time for this epoch 43.27220559120178 No_decrease: 5 ----------------an epoch starts------------------- i_epoch: 156 # batch: 96 i_batch: 0.0 the loss for this batch: 0.11878883 flow loss 0.04531774 occ loss 0.07291381 time for this batch 0.3214073181152344 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.13807546 flow loss 0.04908952 occ loss 0.08839191 time for this batch 0.38690972328186035 ---------------------------------- train loss for this epoch: 0.142351
time for this epoch 46.79589629173279 No_decrease: 6 ----------------an epoch starts------------------- i_epoch: 157 # batch: 96 i_batch: 0.0 the loss for this batch: 0.10662092 flow loss 0.04413603 occ loss 0.061737888 time for this batch 0.3975496292114258 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.15389413 flow loss 0.051899318 occ loss 0.10134258 time for this batch 0.39633607864379883 ---------------------------------- train loss for this epoch: 0.14184
time for this epoch 45.15284013748169 No_decrease: 7 ----------------an epoch starts------------------- i_epoch: 158 # batch: 96 i_batch: 0.0 the loss for this batch: 0.14796619 flow loss 0.052553315 occ loss 0.09472123 time for this batch 0.3361663818359375 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.14693908 flow loss 0.052790217 occ loss 0.09373187 time for this batch 0.418168306350708 ---------------------------------- train loss for this epoch: 0.141844
time for this epoch 45.08456516265869 No_decrease: 8 ----------------an epoch starts------------------- i_epoch: 159 # batch: 96 i_batch: 0.0 the loss for this batch: 0.121077865 flow loss 0.048329398 occ loss 0.07188809 time for this batch 0.3136286735534668 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.15009521 flow loss 0.053725712 occ loss 0.09584291 time for this batch 0.39220476150512695 ---------------------------------- train loss for this epoch: 0.141996
time for this epoch 43.71593260765076 No_decrease: 9 ----------------an epoch starts------------------- i_epoch: 160 # batch: 96 i_batch: 0.0 the loss for this batch: 0.13424717 flow loss 0.050593134 occ loss 0.082915656 time for this batch 0.35251474380493164 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16208906 flow loss 0.05640122 occ loss 0.10488316 time for this batch 0.39744091033935547 ---------------------------------- train loss for this epoch: 0.141748
time for this epoch 43.03426170349121 No_decrease: 10 ----------------an epoch starts------------------- i_epoch: 161 # batch: 96 i_batch: 0.0 the loss for this batch: 0.09105597 flow loss 0.0384658 occ loss 0.051910955 time for this batch 0.313692569732666 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.14691666 flow loss 0.052876927 occ loss 0.09323935 time for this batch 0.3919260501861572 ---------------------------------- train loss for this epoch: 0.141717
time for this epoch 42.86095666885376 No_decrease: 11 ----------------an epoch starts------------------- i_epoch: 162 # batch: 96 i_batch: 0.0 the loss for this batch: 0.17524119 flow loss 0.0589931 occ loss 0.11523739 time for this batch 0.32852983474731445 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.1539074 flow loss 0.052891947 occ loss 0.10043829 time for this batch 0.40260982513427734 ---------------------------------- train loss for this epoch: 0.141742
time for this epoch 44.16611456871033 No_decrease: 12 ----------------an epoch starts------------------- i_epoch: 163 # batch: 96 i_batch: 0.0 the loss for this batch: 0.10873576 flow loss 0.047087256 occ loss 0.061012186 time for this batch 0.32584071159362793 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.1539353 flow loss 0.05444759 occ loss 0.09883302 time for this batch 0.3945794105529785 ---------------------------------- train loss for this epoch: 0.141427
time for this epoch 43.218876361846924 No_decrease: 13 ----------------an epoch starts------------------- i_epoch: 164 # batch: 96 i_batch: 0.0 the loss for this batch: 0.14678064 flow loss 0.057017945 occ loss 0.08901357 time for this batch 0.3404099941253662 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.1364549 flow loss 0.05060737 occ loss 0.08543856 time for this batch 0.3892521858215332 ---------------------------------- train loss for this epoch: 0.141712
time for this epoch 43.60564136505127 No_decrease: 14 ----------------an epoch starts------------------- i_epoch: 165 # batch: 96 i_batch: 0.0 the loss for this batch: 0.15357776 flow loss 0.054088585 occ loss 0.098700136 time for this batch 0.3397200107574463 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.14994733 flow loss 0.05385536 occ loss 0.09544 time for this batch 0.3752717971801758 ---------------------------------- train loss for this epoch: 0.141566
time for this epoch 43.63991117477417 No_decrease: 15 ----------------an epoch starts------------------- i_epoch: 166 # batch: 96 i_batch: 0.0 the loss for this batch: 0.14571331 flow loss 0.054852165 occ loss 0.09009856 time for this batch 0.33571481704711914 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.1262259 flow loss 0.050042804 occ loss 0.07549671 time for this batch 0.40669846534729004 ---------------------------------- train loss for this epoch: 0.141535
time for this epoch 44.236571073532104 No_decrease: 16 ----------------an epoch starts------------------- i_epoch: 167 # batch: 96 i_batch: 0.0 the loss for this batch: 0.14112541 flow loss 0.051998157 occ loss 0.08823089 time for this batch 0.32179975509643555 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16088659 flow loss 0.05332528 occ loss 0.10695385 time for this batch 0.39508557319641113 ---------------------------------- train loss for this epoch: 0.141267
time for this epoch 44.42910385131836 No_decrease: 17 ----------------an epoch starts------------------- i_epoch: 168 # batch: 96 i_batch: 0.0 the loss for this batch: 0.13617744 flow loss 0.048776582 occ loss 0.08660188 time for this batch 0.33379602432250977 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.1434192 flow loss 0.053732745 occ loss 0.08906519 time for this batch 0.3860177993774414 ---------------------------------- train loss for this epoch: 0.141615
time for this epoch 44.222777128219604 No_decrease: 18 ----------------an epoch starts------------------- i_epoch: 169 # batch: 96 i_batch: 0.0 the loss for this batch: 0.15183434 flow loss 0.054702044 occ loss 0.09632134 time for this batch 0.3129267692565918 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.13343075 flow loss 0.049736973 occ loss 0.08287299 time for this batch 0.40874600410461426 ---------------------------------- train loss for this epoch: 0.141255
time for this epoch 43.705711364746094 No_decrease: 19 ----------------an epoch starts------------------- i_epoch: 170 # batch: 96 i_batch: 0.0 the loss for this batch: 0.1699607 flow loss 0.05432343 occ loss 0.11509193 time for this batch 0.33577537536621094 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16536596 flow loss 0.053339954 occ loss 0.111321665 time for this batch 0.39998626708984375 ---------------------------------- train loss for this epoch: 0.141081
time for this epoch 44.52143979072571 No_decrease: 20 ----------------an epoch starts------------------- i_epoch: 171 # batch: 96 i_batch: 0.0 the loss for this batch: 0.13149838 flow loss 0.048763994 occ loss 0.08193328 time for this batch 0.3185093402862549 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16234376 flow loss 0.056190427 occ loss 0.10555648 time for this batch 0.3773157596588135 ---------------------------------- train loss for this epoch: 0.141621
time for this epoch 43.72608017921448 No_decrease: 21 ----------------an epoch starts------------------- i_epoch: 172 # batch: 96 i_batch: 0.0 the loss for this batch: 0.15257734 flow loss 0.05323332 occ loss 0.09852559 time for this batch 0.3616597652435303 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.12217169 flow loss 0.045823388 occ loss 0.075674236 time for this batch 0.39092326164245605 ---------------------------------- train loss for this epoch: 0.141368
time for this epoch 43.08835697174072 No_decrease: 22 ----------------an epoch starts------------------- i_epoch: 173 # batch: 96 i_batch: 0.0 the loss for this batch: 0.11460625 flow loss 0.04512583 occ loss 0.06893029 time for this batch 0.42139458656311035 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.14025128 flow loss 0.052543856 occ loss 0.08698616 time for this batch 0.40236711502075195 ---------------------------------- train loss for this epoch: 0.141103
time for this epoch 45.26199388504028 No_decrease: 23 ----------------an epoch starts------------------- i_epoch: 174 # batch: 96 i_batch: 0.0 the loss for this batch: 0.10978671 flow loss 0.045810986 occ loss 0.063420765 time for this batch 0.33051156997680664 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.13279507 flow loss 0.049355138 occ loss 0.08272226 time for this batch 0.4046497344970703 ---------------------------------- train loss for this epoch: 0.141323
time for this epoch 43.94328308105469 No_decrease: 24 ----------------an epoch starts------------------- i_epoch: 175 # batch: 96 i_batch: 0.0 the loss for this batch: 0.15780497 flow loss 0.05602229 occ loss 0.10114216 time for this batch 0.3114643096923828 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.13358983 flow loss 0.052735936 occ loss 0.08033185 time for this batch 0.3922545909881592 ---------------------------------- train loss for this epoch: 0.141208
time for this epoch 44.878921031951904 No_decrease: 25 ----------------an epoch starts------------------- i_epoch: 176 # batch: 96 i_batch: 0.0 the loss for this batch: 0.15401474 flow loss 0.054444663 occ loss 0.09880937 time for this batch 0.3364086151123047 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.14160213 flow loss 0.051741816 occ loss 0.08890241 time for this batch 0.4008657932281494 ---------------------------------- train loss for this epoch: 0.140736
time for this epoch 44.7962908744812 No_decrease: 26 ----------------an epoch starts------------------- i_epoch: 177 # batch: 96 i_batch: 0.0 the loss for this batch: 0.14342095 flow loss 0.05399645 occ loss 0.08857188 time for this batch 0.33945751190185547 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.14448155 flow loss 0.05036024 occ loss 0.093426645 time for this batch 0.4037461280822754 ---------------------------------- train loss for this epoch: 0.140594
time for this epoch 45.22239637374878 No_decrease: 27 ----------------an epoch starts------------------- i_epoch: 178 # batch: 96 i_batch: 0.0 the loss for this batch: 0.14170152 flow loss 0.051143885 occ loss 0.08980846 time for this batch 0.31815242767333984 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.13293979 flow loss 0.047689162 occ loss 0.08425216 time for this batch 0.38399338722229004 ---------------------------------- train loss for this epoch: 0.141309
time for this epoch 44.28966951370239 No_decrease: 28 ----------------an epoch starts------------------- i_epoch: 179 # batch: 96 i_batch: 0.0 the loss for this batch: 0.13492636 flow loss 0.050229613 occ loss 0.083952494 time for this batch 0.3354008197784424 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.15545292 flow loss 0.05598579 occ loss 0.09883411 time for this batch 0.33438634872436523 ---------------------------------- train loss for this epoch: 0.141057
time for this epoch 42.8647723197937 No_decrease: 29 ----------------an epoch starts------------------- i_epoch: 180 # batch: 96 i_batch: 0.0 the loss for this batch: 0.16261914 flow loss 0.05577989 occ loss 0.106149584 time for this batch 0.3153853416442871 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.12577985 flow loss 0.047966644 occ loss 0.077325545 time for this batch 0.40001702308654785 ---------------------------------- train loss for this epoch: 0.140744
time for this epoch 43.76629972457886 Early stop at the 181-th epoch
def apply_to_vali_test(model, vt, f_o_mean_std):
f = vt["flow"]
f_m = vt["flow_mask"].to(device)
o = vt["occupancy"]
o_m = vt["occupancy_mask"].to(device)
f_mae, f_rmse, o_mae, o_rmse = vali_test(model, f, f_m, o, o_m, f_o_mean_std, hyper["b_s_vt"])
print ("flow_mae", f_mae)
print ("flow_rmse", f_rmse)
print ("occ_mae", o_mae)
print ("occ_rmse", o_rmse)
return f_mae, f_rmse, o_mae, o_rmse
vali_f_mae, vali_f_rmse, vali_o_mae, vali_o_rmse =\
apply_to_vali_test(trained_model, vali, f_o_mean_std)
flow_mae 40.658161587428744 flow_rmse 66.91882702557015 occ_mae 0.03481488251547097 occ_rmse 0.06842147850910055
test_f_mae, test_f_rmse, test_o_mae, test_o_rmse =\
apply_to_vali_test(trained_model, test, f_o_mean_std)
flow_mae 39.388068859146735 flow_rmse 64.68828417712056 occ_mae 0.03058539743703016 occ_rmse 0.06111997924327654